Evidence map›Paper›PMID 31192458›Full record

ArticleThe Journal of physiology2019

Taking advantage of external mechanical work to reduce metabolic cost: the mechanics and energetics of split-belt treadmill walking.

Natalia Sánchez, Surabhi N Simha, J Maxwell Donelan, James M Finley

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of physiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
5.4field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed, 94 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Author response to Panza et al.The Journal of physiology · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Natalia SánchezDivision of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, 90033, USA.ORCID 0000-0002-6467-1781
Surabhi N SimhaDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada, V5A 1S6.
J Maxwell DonelanDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada, V5A 1S6.
James M FinleyDivision of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, 90033, USA.ORCID 0000-0003-2679-2221
Simon Fraser University · CAUniversity of Southern California · US

Funding

Toward a Mechanistic Understanding of Optimization Principles Underlying Hemiparetic GaitR01HD091184 · NICHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI FINLEY, JAMES M. · 2018 to 2022
$1.5M
NICHD NIH HHS R01 HD091184
6 · The paper itself

Abstract

key pointsThe neuromotor system generates flexible motor patterns that can adapt to changes in our bodies or environment and also take advantage of assistance provided by the environment. We ask how energy minimization influences adaptive learning during human locomotion to improve economy when walking on a split-belt treadmill. We use a model-based approach to predict how people should adjust their walking pattern to take advantage of the assistance provided by the treadmill, and we validate these predictions empirically. We show that adaptation to a split-belt treadmill can be explained as a process by which people reduce step length asymmetry to take advantage of the work performed by the treadmill to reduce metabolic cost. Our results also have implications for the evaluation of devices designed to reduce effort during walking, as locomotor adaptation may serve as a model approach to understand how people learn to take advantage of external assistance. ABSTRACT: In everyday tasks such as walking and running, we often exploit the work performed by external sources to reduce effort. Recent research has focused on designing assistive devices capable of performing mechanical work to reduce the work performed by muscles and improve walking function. The success of these devices relies on the user learning to take advantage of this external assistance. Although adaptation is central to this process, the study of adaptation is often done using approaches that seem to have little in common with the use of external assistance. We show in 16 young, healthy participants that a common approach for studying adaptation, split-belt treadmill walking, can be understood from a perspective in which people learn to take advantage of mechanical work performed by the treadmill. Initially, during split-belt walking, people step further forward on the slow belt than the fast belt which we measure as a negative step length asymmetry, but this asymmetry is reduced with practice. We demonstrate that reductions in asymmetry allow people to extract positive work from the treadmill, reduce the positive work performed by the legs, and reduce metabolic cost. We also show that walking with positive step length asymmetries, defined by longer steps on the fast belt, minimizes metabolic cost, and people choose this pattern after guided experience of a wide range of asymmetries. Our results suggest that split-belt adaptation can be interpreted as a process by which people learn to take advantage of mechanical work performed by an external device to improve economy.

Indexed as

Adaptation, PhysiologicalOxygen ConsumptionWalk TestAdultBiomechanical PhenomenaFemaleHumansLearningMalePhysical Conditioning, HumanWalkingassistive deviceslocomotionmechanical workmetabolic costMotor learningsplit-belt walking

Identifiers

PMID31192458
PMCPMC6675650
OpenAlexW2950544525

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.